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1. Dataset.mp4
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MP4
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6.2 MB
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1. Dataset.srt
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SRT
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1 KB
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1. Feed-forward and Back Propagation Networks.mp4
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MP4
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5.8 MB
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1. Feed-forward and Back Propagation Networks.srt
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SRT
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1.2 KB
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1. How artificial neural networks work.mp4
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MP4
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23.2 MB
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1. How artificial neural networks work.srt
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SRT
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3.8 KB
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1. Introduction.mp4
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MP4
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21 MB
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1. Introduction.srt
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SRT
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4.2 KB
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1. Single layer perceptron (SLP) model.mp4
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MP4
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4.8 MB
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1. Single layer perceptron (SLP) model.srt
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SRT
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1.1 KB
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1. What is Gradient Decent.mp4
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MP4
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9.4 MB
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1. What is Gradient Decent.srt
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SRT
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2.1 KB
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1. What is a Deep Learning .mp4
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MP4
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11.6 MB
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1. What is a Deep Learning .srt
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SRT
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3.9 KB
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1. What is the Activation Function.mp4
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MP4
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8.6 MB
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1. What is the Activation Function.srt
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SRT
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1.9 KB
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10. Feature scaling.mp4
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MP4
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23.4 MB
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10. Feature scaling.srt
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SRT
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3.9 KB
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11. Building the Artificial Neural Network.mp4
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MP4
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15.9 MB
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11. Building the Artificial Neural Network.srt
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SRT
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1.9 KB
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12. Adding the input layer and the first hidden layer.mp4
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MP4
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23.5 MB
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12. Adding the input layer and the first hidden layer.srt
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SRT
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3.2 KB
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13. Adding the next hidden layer.mp4
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MP4
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11.2 MB
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13. Adding the next hidden layer.srt
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SRT
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1.3 KB
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14. Adding the output layer.mp4
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MP4
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12.2 MB
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14. Adding the output layer.srt
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SRT
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1.6 KB
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15. Compiling the artificial neural network.mp4
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MP4
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19.6 MB
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15. Compiling the artificial neural network.srt
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SRT
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3 KB
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16. Fitting the ANN model to the training set.mp4
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MP4
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22.4 MB
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16. Fitting the ANN model to the training set.srt
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SRT
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2.3 KB
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17. Predicting the test set results.mp4
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MP4
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25.9 MB
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17. Predicting the test set results.srt
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SRT
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4.7 KB
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2. Advantages of Neural Networks.mp4
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MP4
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4.2 MB
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2. Advantages of Neural Networks.srt
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SRT
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1.2 KB
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2. Anatomy and function of neurons.mp4
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MP4
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7.2 MB
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2. Anatomy and function of neurons.srt
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SRT
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1.4 KB
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2. Backpropagation In Neural Networks.mp4
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MP4
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5.4 MB
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2. Backpropagation In Neural Networks.srt
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SRT
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921.6 B
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2. Components of convolutional neural networks.mp4
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MP4
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5.9 MB
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2. Components of convolutional neural networks.srt
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SRT
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1 KB
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2. Exploring the dataset.mp4
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MP4
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11.5 MB
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2. Exploring the dataset.srt
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SRT
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1.3 KB
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2. Important Terminologies.mp4
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MP4
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4.6 MB
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2. Important Terminologies.srt
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SRT
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716.8 B
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2. Importing libraries.mp4
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MP4
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11.1 MB
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2. Importing libraries.srt
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SRT
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2.6 KB
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2. Radial Basis Network (RBN).mp4
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MP4
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4.4 MB
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2. Radial Basis Network (RBN).srt
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SRT
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921.6 B
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2. What is Stochastic Gradient Decent.mp4
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MP4
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6 MB
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2. What is Stochastic Gradient Decent.srt
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SRT
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2 KB
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2. Why is Deep Learning Important.mp4
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MP4
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7.2 MB
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2. Why is Deep Learning Important.srt
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SRT
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2.1 KB
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3. An introduction to the neural network.mp4
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MP4
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11.5 MB
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3. An introduction to the neural network.srt
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SRT
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3.5 KB
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3. Building the CNN model.mp4
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MP4
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47.6 MB
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3. Building the CNN model.srt
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SRT
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11.4 KB
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3. Convolution Layer.mp4
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MP4
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12 MB
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3. Convolution Layer.srt
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SRT
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3.6 KB
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3. Disadvantages of Neural Networks.mp4
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MP4
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3.4 MB
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3. Disadvantages of Neural Networks.srt
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SRT
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819.2 B
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3. Gradient Decent vs Stochastic Gradient Decent.mp4
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MP4
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6.2 MB
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3. Gradient Decent vs Stochastic Gradient Decent.srt
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SRT
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819.2 B
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3. Minimizing the cost function using backpropagation.mp4
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MP4
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5 MB
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3. Minimizing the cost function using backpropagation.srt
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SRT
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1.6 KB
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3. Multi-layer perceptron (MLP) Neural Network.mp4
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MP4
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4.7 MB
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3. Multi-layer perceptron (MLP) Neural Network.srt
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SRT
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819.2 B
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3. Problem Statement.mp4
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MP4
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3.2 MB
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3. Problem Statement.srt
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SRT
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819.2 B
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3. Software and Frameworks.mp4
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MP4
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5.4 MB
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3. Software and Frameworks.srt
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SRT
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921.6 B
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3. The sigmoid function.mp4
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MP4
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7.1 MB
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3. The sigmoid function.srt
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SRT
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2.3 KB
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4. Accuracy of the model.mp4
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MP4
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8.8 MB
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4. Accuracy of the model.srt
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SRT
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819.2 B
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4. Applications of Neural Networks.mp4
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MP4
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6.4 MB
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4. Applications of Neural Networks.srt
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SRT
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2.1 KB
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4. Architecture of a neural network.mp4
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MP4
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9.1 MB
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4. Architecture of a neural network.srt
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SRT
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1.7 KB
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4. Data Pre-processing.mp4
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MP4
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13.7 MB
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4. Data Pre-processing.srt
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SRT
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4 KB
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4. Hyperbolic tangent function.mp4
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MP4
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6.3 MB
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4. Hyperbolic tangent function.srt
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SRT
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1.3 KB
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4. Pooling Layer.mp4
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MP4
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9.7 MB
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4. Pooling Layer.srt
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SRT
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2.1 KB
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4. Recurrent neural network (RNN).mp4
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MP4
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6 MB
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4. Recurrent neural network (RNN).srt
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SRT
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1.3 KB
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5. Fully connected Layer.mp4
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MP4
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9.4 MB
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5. Fully connected Layer.srt
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SRT
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1.9 KB
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5. Loading the dataset.mp4
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MP4
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9.2 MB
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5. Loading the dataset.srt
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SRT
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1.2 KB
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5. Long Short-Term Memory (LSTM) networks.mp4
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MP4
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6.5 MB
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5. Long Short-Term Memory (LSTM) networks.srt
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SRT
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1.5 KB
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5. Softmax function.mp4
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MP4
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4.2 MB
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5. Softmax function.srt
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SRT
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921.6 B
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6. Hopfield neural network.mp4
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MP4
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5.3 MB
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6. Hopfield neural network.srt
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SRT
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1.3 KB
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6. Rectified Linear Unit (ReLU) function.mp4
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MP4
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5.3 MB
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6. Rectified Linear Unit (ReLU) function.srt
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SRT
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1.5 KB
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6. Splitting the dataset into independent and dependent variables.mp4
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MP4
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22.8 MB
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6. Splitting the dataset into independent and dependent variables.srt
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SRT
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3.2 KB
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7. Boltzmann Machine Neural Network.mp4
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MP4
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4.7 MB
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7. Boltzmann Machine Neural Network.srt
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SRT
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921.6 B
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7. Label encoding using scikit-learn.mp4
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MP4
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28 MB
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7. Label encoding using scikit-learn.srt
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SRT
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4.5 KB
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7. Leaky Rectified Linear Unit function.mp4
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MP4
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4 MB
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7. Leaky Rectified Linear Unit function.srt
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SRT
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921.6 B
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8. One-hot encoding using scikit-learn.mp4
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MP4
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37.9 MB
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8. One-hot encoding using scikit-learn.srt
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SRT
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6.7 KB
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9. Training and Test Sets Splitting Data.mp4
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MP4
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26.5 MB
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9. Training and Test Sets Splitting Data.srt
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SRT
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3.5 KB
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Bonus Resources.txt
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TXT
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409.6 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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